class_docstr: Base class for hooks based on summary metrics.
  
  Subclasses should override _process_metrics.
  
  If _process_metrics returns True, calls run_context.request_stop().
  
  This can be used to something like "Stop after the loss has stopped decreasing
  for 5000 steps.
code: def after_run(self, run_context, run_values):
      global_step = run_values.results[0]
      if (global_step - self._start_step) % self._every_n_steps != 0:
          return
      metrics = self._collect_metrics()
      self._after_run(run_context, run_values, global_step, metrics)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow.compat.v1
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import event_multiplexer
label_desc: Use image node_id 6 for calling the MetricsBasedHook obj's underlying member method code with example usage: obj.after_run(run_context, run_values) without return types